GG20 Donations made through the IDriss Browser Extension¶
Imports¶
In [1]:
import pandas as pd
import requests
from web3 import Web3
from tabulate import tabulate
from eth_abi import decode_single, decode_abi
import plotly.graph_objects as go
import matplotlib.pyplot as plt
Constants and Functions¶
In [2]:
ACROSS_REQUEST_URL = "https://api.across.to/deposits/details"
def to_eth(value):
return value / 10**18
Cross-Chain Donations¶
In [3]:
def decode_data(hex_str):
data_bytes = bytes.fromhex(hex_str[2:])
return decode_abi(
['address', 'address', 'uint256', 'address', 'uint256', 'address'],
data_bytes
)
In [4]:
def fetch_deposit_details(tx_hash, origin_chain_id):
params = {
'depositTxHash': tx_hash,
'originChainId': origin_chain_id
}
response = requests.get(ACROSS_REQUEST_URL, params=params)
result = {
'status': None,
'message': None,
'fillTxhash': None,
'destination_chain': None
}
if response.status_code == 200:
data = response.json()
result['status'] = data.get('status')
result['message'] = data.get('message')
result['destination_chain'] = data.get('destinationChainId')
fill_txs = data.get('fillTxs', [])
if fill_txs:
result['fillTxhash'] = fill_txs[0].get('hash')
return result
Importing .csv files¶
The attestation files are downloaded .csv files from EAS' indexer as described here: https://github.com/idriss-crypto/browser-extensions/blob/master/CONTRACTS.md
The wrapper .csv files are downloaded transactions from our wrapper contracts. We used the download functionality on the respective block explorer and filtered for the date of the GG20 round before importing the files here. The contract addresses (and links to the block explorer) can be found in the Gitcoin GG20 Donations section here: https://github.com/idriss-crypto/browser-extensions/tree/master
In [5]:
arbitrum_attestations = 'attestations_arbitrum.csv'
optimism_attestations = 'attestations_optimism.csv'
arbitrum_wrapper = 'arbitrum_wrapper.csv'
optimism_wrapper = 'optimism_wrapper.csv'
ethereum_wrapper = 'ethereum_wrapper.csv'
base_wrapper = 'base_wrapper.csv'
linea_wrapper = 'linea_wrapper.csv'
zksync_wrapper = 'zksync_wrapper.csv'
In [6]:
relevant_columns_attestattion = ['attester', 'data', 'recipient', 'txid', 'id']
df_attestations_arb = pd.read_csv(arbitrum_attestations, index_col=False)[relevant_columns_attestattion]
df_attestations_op = pd.read_csv(optimism_attestations, index_col=False)[relevant_columns_attestattion]
relevant_columns_wrapper = ['Txhash', 'From', 'To', 'Method']
df_wrapper_arb = pd.read_csv(arbitrum_wrapper, index_col=False)[relevant_columns_wrapper]
df_wrapper_op = pd.read_csv(optimism_wrapper, index_col=False)[relevant_columns_wrapper]
df_wrapper_eth = pd.read_csv(ethereum_wrapper, index_col=False)[relevant_columns_wrapper]
df_wrapper_base = pd.read_csv(base_wrapper, index_col=False)[relevant_columns_wrapper]
df_wrapper_linea = pd.read_csv(linea_wrapper, index_col=False)[relevant_columns_wrapper]
df_wrapper_zksync = pd.read_csv(zksync_wrapper, index_col=False)[relevant_columns_wrapper]
In [7]:
method_hex = '0x6fde4731'
method_name = 'Call Deposit V3'
df_wrapper_arb['Method'] = df_wrapper_arb['Method'].replace(method_hex, method_name)
df_wrapper_op['Method'] = df_wrapper_op['Method'].replace(method_hex, method_name)
df_wrapper_eth['Method'] = df_wrapper_eth['Method'].replace(method_hex, method_name)
df_wrapper_base['Method'] = df_wrapper_base['Method'].replace(method_hex, method_name)
df_wrapper_linea['Method'] = df_wrapper_linea['Method'].replace(method_hex, method_name)
df_wrapper_zksync['Method'] = df_wrapper_zksync['Method'].replace(method_hex, method_name)
In [8]:
df_wrapper_arb = df_wrapper_arb.loc[df_wrapper_arb['Method'] == method_name]
df_wrapper_op = df_wrapper_op.loc[df_wrapper_op['Method'] == method_name]
df_wrapper_eth = df_wrapper_eth.loc[df_wrapper_eth['Method'] == method_name]
df_wrapper_base = df_wrapper_base.loc[df_wrapper_base['Method'] == method_name]
df_wrapper_linea = df_wrapper_linea.loc[df_wrapper_linea['Method'] == method_name]
df_wrapper_zksync = df_wrapper_zksync.loc[df_wrapper_zksync['Method'] == method_name]
In [9]:
df_attestations_arb['destination_chain'] = '42161'
df_attestations_op['destination_chain'] = '10'
df_wrapper_arb['origin_chain'] = '42161'
df_wrapper_op['origin_chain'] = '10'
df_wrapper_eth['origin_chain'] = '1'
df_wrapper_base['origin_chain'] = '8453'
df_wrapper_linea['origin_chain'] = '59144'
df_wrapper_zksync['origin_chain'] = '324'
df_attestations = pd.concat([df_attestations_arb, df_attestations_op], ignore_index=True)
df_wrapper = pd.concat([df_wrapper_arb, df_wrapper_op, df_wrapper_eth, df_wrapper_base, df_wrapper_linea, df_wrapper_zksync], ignore_index=True)
df_attestations = df_attestations.rename(columns={'recipient':'attestation_recipient', 'txid':'Txhash', 'id':'uid'})
In [10]:
df_wrapper['status'] = None
df_wrapper['message'] = None
df_wrapper['fillTxhash'] = None
df_wrapper['destination_chain'] = None
# Iterate through each row and update the new columns based on API data
for index, row in df_wrapper.iterrows():
deposit_tx_hash = row['Txhash']
origin_chain_id = row['origin_chain']
# Fetch details and extract relevant fields
details = fetch_deposit_details(deposit_tx_hash, origin_chain_id)
# Assign the extracted values to the appropriate columns
df_wrapper.at[index, 'status'] = details['status']
df_wrapper.at[index, 'message'] = details['message']
df_wrapper.at[index, 'fillTxhash'] = details['fillTxhash']
df_wrapper.at[index, 'destination_chain'] = str(details['destination_chain'])
In [11]:
# Confirmed error in the Across API:
# Txhash from attestation/donation: 0x7798d6ceb4f3f18f377c15980a2e19ac37f1c75d6e0f7d5f4a0ce8908337d76a
# Has a true corresponding deposit from Optimism at 0x9d83208add1a5517dd53e6ba392c66e36ec876317a1b39861c7eb9980fbf420a
correction = {
'status': 'filled',
'fillTxhash': '0x7798d6ceb4f3f18f377c15980a2e19ac37f1c75d6e0f7d5f4a0ce8908337d76a',
'destination_chain': 42161,
'message': '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'
}
index_to_update = df_wrapper.loc[
(df_wrapper['Txhash'] == '0x9d83208add1a5517dd53e6ba392c66e36ec876317a1b39861c7eb9980fbf420a') &
(df_wrapper['origin_chain'] == '10')
].index
if not index_to_update.empty:
df_wrapper.at[index_to_update[0], 'status'] = correction['status']
df_wrapper.at[index_to_update[0], 'message'] = correction['message']
df_wrapper.at[index_to_update[0], 'fillTxhash'] = correction['fillTxhash']
df_wrapper.at[index_to_update[0], 'destination_chain'] = str(correction['destination_chain'])
In [12]:
gg20_rounds = {
'42161': [23, 24, 25, 26, 27, 28, 29, 31],
'10': [9]
}
df_attestations[['donor', 'recipient_id', 'round_id', 'token_sent', 'amount', 'origin']] = df_attestations['data'].apply(
lambda x: pd.Series(decode_data(x))
)
def is_gg20_round(row):
chain = row['destination_chain']
round_id = row['round_id']
return chain in gg20_rounds and round_id in gg20_rounds[chain]
df_attestations['gg20_round'] = df_attestations.apply(is_gg20_round, axis=1)
df_attestations = df_attestations[df_attestations['gg20_round']]
df_attestations.drop(columns='gg20_round', inplace=True)
attestations_final_df = pd.merge(df_attestations, df_wrapper, left_on='Txhash', right_on='fillTxhash', how='left', indicator=True)
attestations_final_df.drop(columns=['_merge', 'Method', 'token_sent', 'status', 'destination_chain_y'], inplace=True)
attestations_final_df = attestations_final_df.rename(columns={'destination_chain_x':'destination_chain', 'Txhash_x':'Txhash_destination', 'Txhash_y':'Txhash_origin'})
In [13]:
print(attestations_final_df.head())
In [14]:
num_unique_donors = attestations_final_df['donor'].nunique()
num_unique_recipients = attestations_final_df['recipient_id'].nunique()
avg_amount = attestations_final_df['amount'].mean()
median_amount = attestations_final_df['amount'].median()
total_amount = attestations_final_df['amount'].sum()
top_donor = attestations_final_df['donor'].value_counts().idxmax()
top_recipient = attestations_final_df['recipient_id'].value_counts().idxmax()
top_donor_donations = attestations_final_df[attestations_final_df['donor'] == top_donor].shape[0]
top_recipient_donations = attestations_final_df[attestations_final_df['recipient_id'] == top_recipient].shape[0]
avg_donations_by_donor = attestations_final_df.groupby('donor')['amount'].mean()
median_donations_by_donor = attestations_final_df.groupby('donor')['amount'].median()
avg_donations_by_recipient = attestations_final_df.groupby('recipient_id')['amount'].mean()
median_donations_by_recipient = attestations_final_df.groupby('recipient_id')['amount'].median()
round_id_counts = attestations_final_df['round_id'].value_counts()
total_donations = attestations_final_df.shape[0]
statistics = {
'Total Donations': total_donations,
'Number of Unique Donors': num_unique_donors,
'Number of Unique Recipients': num_unique_recipients,
'Average Amount': to_eth(avg_amount),
'Median Amount': to_eth(median_amount),
'Total Amount': to_eth(total_amount),
'Top Donor': top_donor,
'Top Recipient': top_recipient,
'Number of Donations by Top Donor': top_donor_donations,
'Number of Donations Received by Top Recipient': top_recipient_donations,
'Average Donations by Donor': to_eth(avg_donations_by_donor.mean()),
'Median Donations by Donor': to_eth(median_donations_by_donor.median()),
'Average Donations by Recipient': to_eth(avg_donations_by_recipient.mean()),
'Median Donations by Recipient': to_eth(median_donations_by_recipient.median())
}
statistics_df = pd.DataFrame.from_dict(statistics, orient='index', columns=['Value'])
print("\nStatistics for Cross-Chain Donations:")
print(statistics_df)
In [15]:
transactions_by_origin = attestations_final_df.groupby('origin_chain').agg(
transaction_count=('amount', 'count'),
total_amount=('amount', 'sum'),
average_amount=('amount', 'mean'),
median_amount=('amount', 'median')
)
transactions_by_origin.reset_index(inplace=True)
transactions_by_origin[['total_amount', 'average_amount', 'median_amount']] = \
transactions_by_origin[['total_amount', 'average_amount', 'median_amount']].applymap(to_eth).round(6)
print("\nTransactions by Origin Chain:")
print(tabulate(transactions_by_origin, headers='keys', tablefmt='pretty', showindex=False, colalign=('right', 'center', 'right', 'right', 'right')))
In [16]:
round_id_counts = attestations_final_df['round_id'].value_counts().reset_index()
round_id_counts.columns = ['round_id', 'count'] # Rename columns for clarity
round_id_counts.sort_values(by='round_id', inplace=True)
total_donations = round_id_counts['count'].sum()
round_id_counts['percentage'] = round_id_counts['count'] / total_donations * 100
round_id_counts['percentage'] = round_id_counts['percentage'].apply(lambda x: f"{x:.2f}%")
print("\nRound ID Counts Table for Cross-Chain Donations:")
print(tabulate(round_id_counts, headers='keys', tablefmt='pretty', showindex=False, colalign=('right', 'right', 'right')))
In [17]:
origin_chain_counts = attestations_final_df['origin_chain'].value_counts()
plt.figure(figsize=(8, 8))
plt.pie(origin_chain_counts, labels=origin_chain_counts.index, autopct='%1.1f%%', startangle=140)
plt.title('Distribution of Origin Chains')
plt.axis('equal')
plt.show()
destination_chain_counts = attestations_final_df['destination_chain'].value_counts()
plt.figure(figsize=(8, 8))
plt.pie(destination_chain_counts, labels=destination_chain_counts.index, autopct='%1.1f%%', startangle=140)
plt.title('Distribution of Destination Chains')
plt.axis('equal')
plt.show()
round_id_counts = attestations_final_df['round_id'].value_counts()
plt.figure(figsize=(8, 8))
plt.pie(
round_id_counts,
labels=round_id_counts.index,
autopct='%1.1f%%',
startangle=140
)
plt.title('Distribution of Round ID Counts')
plt.axis('equal')
plt.show()
top_donors = attestations_final_df['donor'].value_counts().head(10)
plt.figure(figsize=(10, 6))
plt.bar(top_donors.index, top_donors.values)
plt.xlabel('Top Donors')
plt.ylabel('Count')
plt.title('Top 10 Donors')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y')
plt.show()
top_recipients = attestations_final_df['recipient_id'].value_counts().head(10)
plt.figure(figsize=(10, 6))
plt.bar(top_recipients.index, top_recipients.values)
plt.xlabel('Top Recipients')
plt.ylabel('Count')
plt.title('Top 10 Recipients')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y')
plt.show()
amounts = attestations_final_df['amount'] / 10**18
plt.figure(figsize=(10, 6))
plt.hist(amounts, bins=60, edgecolor='black')
plt.xlabel('Amount (ETH)')
plt.ylabel('Frequency')
plt.title('Distribution of Amounts')
plt.grid(axis='y')
Same Chain Donations¶
In [18]:
provider_arb = Web3.HTTPProvider("https://arb1.arbitrum.io/rpc")
provider_op = Web3.HTTPProvider("https://mainnet.optimism.io")
w3_arb = Web3(provider_arb)
w3_op = Web3(provider_op)
The {chain}_allo.csv files are downloaded transactions from the Arbitrum and Optimism block explorer, filtered by the dates of GG20. We are not aware of another direct contract integration and assume that all direct allocate() calls were made through our extension. The traditional checkout uses a multi-checkout contract, which makes it so the allocations show up as internal transactions (and therefore not in the following data frames).
In [19]:
arbitrum_allo = 'arbitrum_allo.csv'
optimism_allo = 'optimism_allo.csv'
relevant_columns_attestattion = ['Txhash', 'From', 'Method', 'Value_IN(ETH)', 'Status']
df_allo_arb = pd.read_csv(arbitrum_allo, index_col=False)[relevant_columns_attestattion]
df_allo_op = pd.read_csv(optimism_allo, index_col=False)[relevant_columns_attestattion]
df_allo_arb['origin_chain'] = 42161
df_allo_op['origin_chain'] = 10
df_allo_arb['destination_chain'] = 42161
df_allo_op['destination_chain'] = 10
df_allo = pd.concat([df_allo_arb, df_allo_op], ignore_index=True)
df_allo = df_allo.loc[df_allo['Method'] == 'Allocate']
df_allo = df_allo.loc[df_allo['Status'] != 'Error(0)']
df_allo.drop(columns='Status', inplace=True)
In [20]:
allocate_event_signature = '0xdc9d40760308557d1377c2fe7c984ace9eb02d23b60a5f6f26be62c52431bc38'
allocated_event_abi = [
{"indexed": True, "name": "recipientId", "type": "address"},
{"indexed": False, "name": "amount", "type": "uint256"},
{"indexed": False, "name": "token", "type": "address"},
{"indexed": False, "name": "sender", "type": "address"},
{"indexed": False, "name": "origin", "type": "address"}
]
gg20_rounds = {
'42161': [23, 24, 25, 26, 27, 28, 29, 31],
'10': [9]
}
def decode_tx_data_and_event(row):
tx_hash = row['Txhash']
origin_chain = str(row['origin_chain'])
if origin_chain == '42161':
w3 = w3_arb
elif origin_chain == '10':
w3 = w3_op
else:
return None
try:
tx = w3.eth.get_transaction(tx_hash)
tx_receipt = w3.eth.get_transaction_receipt(tx_hash)
except Exception as e:
print(f"Error fetching receipt for {tx_hash}: {e}")
return None
try:
input_data = tx.input[10:]
decoded_input = decode_abi(['uint256', 'bytes'], bytes.fromhex(input_data))
round_id = decoded_input[0]
except Exception as e:
print(f"Error decoding input data for {tx_hash}: {e}")
round_id = None
allocated_event_log = None
for log in tx_receipt.logs:
if log['topics'][0].hex() == allocate_event_signature:
allocated_event_log = log
break
if allocated_event_log:
recipient_id_raw = allocated_event_log['topics'][1].hex()
recipient_id = w3.toChecksumAddress('0x' + recipient_id_raw[-40:])
non_indexed_data = decode_abi(
['uint256', 'address', 'address', 'address'],
bytes.fromhex(allocated_event_log['data'][2:])
)
amount, token, sender, origin = non_indexed_data
is_gg20_round = round_id in gg20_rounds.get(origin_chain, [])
return {
'round_id': round_id,
'recipient_id': recipient_id,
'amount': amount,
'token': token,
'donor': sender,
'origin': origin,
'is_gg20_round': is_gg20_round,
}
else:
return {
'round_id': round_id,
'recipient_id': None,
'amount': None,
'token': None,
'donor': None,
'origin': None,
'is_gg20_round': False,
}
In [21]:
new_columns = df_allo.apply(lambda row: pd.Series(decode_tx_data_and_event(row)), axis=1)
df_allo = pd.concat([df_allo, new_columns], axis=1)
In [22]:
df_allo = df_allo[df_allo['is_gg20_round']]
df_allo.drop(columns='is_gg20_round', inplace=True)
In [23]:
columns_to_check = ['round_id', 'recipient_id', 'amount', 'token', 'donor', 'origin']
missing_values_df = df_allo[df_allo[columns_to_check].isnull().any(axis=1)]
print("\nTransactions with None or missing values:")
print(missing_values_df)
In [24]:
num_unique_donors_same_chain = df_allo['donor'].nunique()
num_unique_recipients_same_chain = df_allo['recipient_id'].nunique()
avg_amount_same_chain = df_allo['amount'].mean()
median_amount_same_chain = df_allo['amount'].median()
total_amount_same_chain = df_allo['amount'].sum()
top_donor_same_chain = df_allo['donor'].value_counts().idxmax()
top_recipient_same_chain = df_allo['recipient_id'].value_counts().idxmax()
top_donor_donations_same_chain = df_allo[df_allo['donor'] == top_donor].shape[0]
top_recipient_donations_same_chain = df_allo[df_allo['recipient_id'] == top_recipient_same_chain].shape[0]
avg_donations_by_donor_same_chain = df_allo.groupby('donor')['amount'].mean()
median_donations_by_donor_same_chain = df_allo.groupby('donor')['amount'].median()
avg_donations_by_recipient_same_chain = df_allo.groupby('recipient_id')['amount'].mean()
median_donations_by_recipient_same_chain = df_allo.groupby('recipient_id')['amount'].median()
round_id_counts_same_chain = df_allo['round_id'].value_counts()
total_donations_same_chain = df_allo.shape[0]
statistics_same_chain = {
'Total Donations': total_donations_same_chain,
'Number of Unique Donors': num_unique_donors_same_chain,
'Number of Unique Recipients': num_unique_recipients_same_chain,
'Average Amount': to_eth(avg_amount_same_chain),
'Median Amount': to_eth(median_amount_same_chain),
'Total Amount': to_eth(total_amount_same_chain),
'Top Donor': top_donor_same_chain,
'Top Recipient': top_recipient_same_chain,
'Number of Donations by Top Donor': top_donor_donations_same_chain,
'Number of Donations Received by Top Recipient': top_recipient_donations_same_chain,
'Average Donations by Donor': to_eth(avg_donations_by_donor_same_chain.mean()),
'Median Donations by Donor': to_eth(median_donations_by_donor_same_chain.median()),
'Average Donations by Recipient': to_eth(avg_donations_by_recipient_same_chain.mean()),
'Median Donations by Recipient': to_eth(median_donations_by_recipient_same_chain.median())
}
statistics_df_same_chain = pd.DataFrame.from_dict(statistics_same_chain, orient='index', columns=['Value'])
print("\nStatistics for Same Chain Donations:")
print(statistics_df_same_chain)
In [25]:
round_id_counts_same_chain = df_allo['round_id'].value_counts().reset_index()
round_id_counts_same_chain.columns = ['round_id', 'count'] # Rename columns for clarity
round_id_counts_same_chain.sort_values(by='round_id', inplace=True)
total_donations = round_id_counts_same_chain['count'].sum()
round_id_counts_same_chain['percentage'] = round_id_counts_same_chain['count'] / total_donations * 100
round_id_counts_same_chain['percentage'] = round_id_counts_same_chain['percentage'].apply(lambda x: f"{x:.2f}%")
print("\nRound ID Counts Table:")
print(tabulate(round_id_counts_same_chain, headers='keys', tablefmt='pretty', showindex=False, colalign=('right', 'right', 'right')))
In [26]:
origin_chain_counts_same_chain = df_allo['origin_chain'].value_counts()
plt.figure(figsize=(8, 8))
plt.pie(origin_chain_counts_same_chain, labels=origin_chain_counts_same_chain.index, autopct='%1.1f%%', startangle=140)
plt.title('Distribution of Origin Chains')
plt.axis('equal')
plt.show()
round_id_counts_same_chain = df_allo['round_id'].value_counts()
plt.figure(figsize=(8, 8))
plt.pie(
round_id_counts_same_chain,
labels=round_id_counts_same_chain.index,
autopct='%1.1f%%',
startangle=140
)
plt.title('Distribution of Round ID Counts')
plt.axis('equal')
plt.show()
top_donors_same_chain = df_allo['donor'].value_counts().head(10)
plt.figure(figsize=(10, 6))
plt.bar(top_donors_same_chain.index, top_donors_same_chain.values)
plt.xlabel('Top Donors')
plt.ylabel('Count')
plt.title('Top 10 Donors')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y')
plt.show()
top_recipients_same_chain = df_allo['recipient_id'].value_counts().head(10)
plt.figure(figsize=(10, 6))
plt.bar(top_recipients_same_chain.index, top_recipients_same_chain.values)
plt.xlabel('Top Recipients')
plt.ylabel('Count')
plt.title('Top 10 Recipients')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y')
plt.show()
amounts_same_chain = df_allo['amount'] / 10**18
plt.figure(figsize=(10, 6))
plt.hist(amounts_same_chain, bins=60, edgecolor='black')
plt.xlabel('Amount (ETH)')
plt.ylabel('Frequency')
plt.title('Distribution of Amounts')
plt.grid(axis='y')
Combined¶
In [27]:
df_combined = pd.concat([df_allo, attestations_final_df], axis=0, ignore_index=True)
print(df_combined.head())
In [28]:
num_unique_donors_combined = df_combined['donor'].nunique()
num_unique_recipients_combined = df_combined['recipient_id'].nunique()
avg_amount_combined = df_combined['amount'].mean()
median_amount_combined = df_combined['amount'].median()
total_amount_combined = df_combined['amount'].sum()
top_donor_combined = df_combined['donor'].value_counts().idxmax()
top_recipient_combined = df_combined['recipient_id'].value_counts().idxmax()
top_donor_donations_combined = df_combined[df_combined['donor'] == top_donor_combined].shape[0]
top_recipient_donations_combined = df_combined[df_combined['recipient_id'] == top_recipient_combined].shape[0]
avg_donations_by_donor_combined = df_combined.groupby('donor')['amount'].mean()
median_donations_by_donor_combined = df_combined.groupby('donor')['amount'].median()
avg_donations_by_recipient_combined = df_combined.groupby('recipient_id')['amount'].mean()
median_donations_by_recipient_combined = df_combined.groupby('recipient_id')['amount'].median()
round_id_counts_combined = df_combined['round_id'].value_counts()
total_donations_combined = df_combined.shape[0]
statistics_combined = {
'Total Donations': total_donations_combined,
'Number of Unique Donors': num_unique_donors_combined,
'Number of Unique Recipients': num_unique_recipients_combined,
'Average Amount': to_eth(avg_amount_combined),
'Median Amount': to_eth(median_amount_combined),
'Total Amount': to_eth(total_amount_combined),
'Top Donor': top_donor_combined,
'Top Recipient': top_recipient_combined,
'Number of Donations by Top Donor': top_donor_donations_combined,
'Number of Donations Received by Top Recipient': top_recipient_donations_combined,
'Average Donations by Donor': to_eth(avg_donations_by_donor_combined.mean()),
'Median Donations by Donor': to_eth(median_donations_by_donor_combined.median()),
'Average Donations by Recipient': to_eth(avg_donations_by_recipient_combined.mean()),
'Median Donations by Recipient': to_eth(median_donations_by_recipient_combined.median())
}
statistics_df_combined = pd.DataFrame.from_dict(statistics_combined, orient='index', columns=['Value'])
print("\nCombined Statistics:")
print(statistics_df_combined)
In [29]:
transactions_by_origin_combined = df_combined.groupby('origin_chain').agg(
transaction_count=('amount', 'count'),
total_amount=('amount', 'sum'),
average_amount=('amount', 'mean'),
median_amount=('amount', 'median')
)
transactions_by_origin_combined.reset_index(inplace=True)
transactions_by_origin_combined[['total_amount', 'average_amount', 'median_amount']] = \
transactions_by_origin_combined[['total_amount', 'average_amount', 'median_amount']].applymap(to_eth).round(6)
print("\nCombined Transactions by Origin Chain:")
print(tabulate(transactions_by_origin_combined, headers='keys', tablefmt='pretty', showindex=False, colalign=('right', 'right', 'right', 'right', 'right')))
In [30]:
round_id_counts_combined = df_combined['round_id'].value_counts().reset_index()
round_id_counts_combined.columns = ['round_id', 'count'] # Rename columns for clarity
total_donations = round_id_counts_combined['count'].sum()
round_id_counts_combined['percentage'] = round_id_counts_combined['count'] / total_donations * 100
round_id_counts_combined['percentage'] = round_id_counts_combined['percentage'].apply(lambda x: f"{x:.2f}%")
round_id_counts_combined.sort_values(by='round_id', inplace=True)
print("\nCombined Round ID Counts Table:")
print(tabulate(round_id_counts_combined, headers='keys', tablefmt='pretty', showindex=False, colalign=('right', 'right', 'right')))
In [31]:
origin_chain_counts_combined = df_combined['origin_chain'].value_counts()
plt.figure(figsize=(8, 8))
plt.pie(origin_chain_counts_combined, labels=origin_chain_counts_combined.index, autopct='%1.1f%%', startangle=140)
plt.title('Distribution of Origin Chains')
plt.axis('equal')
plt.show()
destination_chain_counts_combined = df_combined['destination_chain'].value_counts()
plt.figure(figsize=(8, 8))
plt.pie(destination_chain_counts_combined, labels=destination_chain_counts_combined.index, autopct='%1.1f%%', startangle=140)
plt.title('Distribution of Destination Chains')
plt.axis('equal')
plt.show()
round_id_counts_combined = df_combined['round_id'].value_counts()
plt.figure(figsize=(8, 8))
plt.pie(
round_id_counts_combined,
labels=round_id_counts_combined.index,
autopct='%1.1f%%',
startangle=140
)
plt.title('Distribution of Round ID Counts')
plt.axis('equal')
plt.show()
top_donors_combined = df_combined['donor'].value_counts().head(10)
plt.figure(figsize=(10, 6))
plt.bar(top_donors_combined.index, top_donors_combined.values)
plt.xlabel('Top Donors')
plt.ylabel('Count')
plt.title('Top 10 Donors')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y')
plt.show()
top_recipients_combined = df_combined['recipient_id'].value_counts().head(10)
plt.figure(figsize=(10, 6))
plt.bar(top_recipients_combined.index, top_recipients_combined.values)
plt.xlabel('Top Recipients')
plt.ylabel('Count')
plt.title('Top 10 Recipients')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y')
plt.show()
amounts_combined = df_combined['amount'] / 10**18 # Adjust this if amounts are already in ETH
plt.figure(figsize=(10, 6))
plt.hist(amounts_combined, bins=60, edgecolor='black') # Adjust the number of bins as needed
plt.xlabel('Amount (ETH)')
plt.ylabel('Frequency')
plt.title('Distribution of Amounts')
plt.grid(axis='y')
In [32]:
all_chains_combined = pd.concat([df_combined['origin_chain'], df_combined['destination_chain']]).unique()
chain_to_idx_combined = {chain: idx for idx, chain in enumerate(all_chains_combined)}
df_combined['origin_chain'] = df_combined['origin_chain'].astype(str)
df_combined['destination_chain'] = df_combined['destination_chain'].astype(str)
chain_names = {
'42161': 'Arbitrum',
'324': 'zkSync Era',
'8453': 'Base',
'59144': 'Linea',
'10': 'Optimism',
'1': 'Ethereum'
}
chain_colors = {
'42161': '#0033ad',
'324': '#76e0f7',
'8453': '#4d88ff',
'59144': '#505050',
'10': '#ff6961',
'1': '#627eea'
}
df_combined['origin_chain_name'] = df_combined['origin_chain'].map(chain_names)
df_combined['destination_chain_name'] = df_combined['destination_chain'].map(chain_names)
df_combined['origin_color'] = df_combined['origin_chain'].map(chain_colors)
df_combined['destination_color'] = df_combined['destination_chain'].map(chain_colors)
unique_chains = pd.concat([df_combined['origin_chain_name'], df_combined['destination_chain_name']]).unique()
chain_to_idx_origin = {chain: idx for idx, chain in enumerate(unique_chains)}
chain_to_idx_destination = {chain: idx + len(unique_chains) for idx, chain in enumerate(unique_chains)}
sources = df_combined['origin_chain_name'].map(chain_to_idx_origin)
targets = df_combined['destination_chain_name'].map(chain_to_idx_destination)
values = [1] * len(df_combined)
link_colors = df_combined['origin_color'].tolist()
node_colors = [chain_colors[next(key for key, value in chain_names.items() if value == name)] for name in unique_chains]
labels = [name for name in unique_chains] + [name for name in unique_chains]
fig = go.Figure(go.Sankey(
node=dict(
pad=15,
thickness=20,
line=dict(color="black", width=0.5),
label=labels,
color=node_colors
),
link=dict(
source=sources,
target=targets,
value=values,
color=link_colors
)
))
fig.update_layout(
title_text="Flow of Transactions",
font_size=12,
annotations=[
dict(
text="Origin",
x=0.05, y=1.1,
showarrow=False,
font=dict(size=16, color='black')
),
dict(
text="Destination",
x=0.95, y=1.1,
showarrow=False,
font=dict(size=16, color='black')
)
]
)
fig.show()
In [33]:
df_allo.to_csv('df_allo.csv', index=False)
attestations_final_df.to_csv('attestations_final_df.csv', index=False)
df_combined.to_csv('df_combined.csv', index=False)